Tail Risk and Corporate Bond Returns
DescriptionThe study of rare disaster risk or tail risk in the asset pricing literature can be dated back to Rietz (1988), Kahneman et al. (1990), and Barro (2006). It has been widely documented that extreme events could potentially explain some empirical puzzles like equity risk premium and low-interest rate. The experience of the recent global financial crisis redraws researchers’ attention to accurately assess the tail risk as well as its predictability on equity or option returns (Bollerslev and Todorov 2011a, b; Bali et al., 2014; Kelly and Jiang, 2014). In general, investors request an additional positive premium for bearing the extreme event risk.Although been extensively recognized in the equity market, the studies on the pricing of tail risk in the corporate bond market are few. Chava et al. (2014) address bond offering yield is positively related to the financial institution’s tail risk but only identifies the tail risk of each issuer. Li and Song (2014) introduce a tail risk measure based on interest rate swaption. Bai et al. (2019) consider a downside risk factor via aggregating idiosyncratic risk displayed in the individual bonds, which exposes high liquidity requirement of bond trades. In this project, we aim to find an appropriate measure of tail risk and thoroughly investigate the pricing pattern of systematic tail risk in the context of the corporate bond market.In particular, we plan to propose two tail-risk measures directly utilizing the bond returns. One emphasizes the risk perceptions of corporate bond investors by following Kelly and Jiang (2014) that pools all monthly bond returns to capture the tail behavior. The other concentrates on the perspective of dynamic statistical assessment similar to Acemoglu et al. (2017) that describes the left tail behavior that deviated from the standard normal distribution. With these two factors, we then comprehensively address the predictability of tail risk in the cross-section of bond returns. We conjecture that bonds more sensitive to past tail risk exhibit higher expected cross-section returns as investors require compensation for bearing the extreme uncertainty. Standard empirical asset pricing methodology will be adopted. By conducting portfolio analysis (univariate sorting by tail risk beta, bivariate / trivariate sorting controlling for well-documented pricing effect in fixed-income markets and bond characteristics), as well as Fama and MacBeth (1973) cross-section regression with various model specifications, we makeefforts in explaining the research question, whether and to what extent is tail risk priced in the corporate bond returns.Our project is contributive in that we are the first to propose a systematic tail risk measure applicable to the corporate bond data by taking into account the distinct attributes of corporate bond trading (for example, the rarity of liquidity and dominated by institutional investors). Unlike stockholders, bondholders are more exposed to the downside part of tail risk, and their upside cash flow payoffs are capped (Hong and Sraer, 2013). Our findings on how cross-section bond returns are affected by tail risk conditional on credit risk provide significant implications to bond investors when making their hedge strategies and portfolio allocation. Moreover, our studycomplements the asset pricing framework by shedding light upon the sources of tail risk pricing in corporate bonds and how such impact varies regarding different types of bonds and investors.
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